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Code Interpreter by OpenAI
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Code Generation & Assistants (171)

Code Interpreter by OpenAI Verified Tool

Code Interpreter by OpenAI: Explore features, use cases, pricing, pros and cons to see whether this tool fits your workflow.

Last Update: August 20, 2026

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Starting price Free + from $20/mo

Tool Information

ChatGPT can analyze files, run code in a sandboxed environment, create charts and help explain, transform or debug programming work through its data-analysis tools. Users should remove secrets and unnecessary personal data, verify calculations and generated files, review code and dependencies, run production tests separately, monitor retention settings and retain human analytical and engineering responsibility.

Use authorized, minimal inputs and grant integrations the least access required. Configure privacy, retention, sharing, quality, accessibility, disclosure, export, governance, moderation and spending controls. Test representative cases, verify generated facts, calculations, citations, customer messages, code, trades and financial details, preserve originals and version history, and retain accountable human approval before publication, outreach, deployment or operational action.

A free ChatGPT plan is available and paid individual access starts from approximately $20 per month, with business and enterprise options. Models, messages, file uploads, analysis capacity, subscription period, renewal and taxes vary.

AI output can be inaccurate, biased, derivative, insecure, incomplete or misleading. Review consent, copyright, financial risk, training and retention terms, renewals, refunds, platform rules and applicable law. Medical, education, finance, investment, compliance, marketing, travel and customer-facing workflows require qualified human review.

F.A.Q (3)

ChatGPT can analyze files, run code in a sandboxed environment, create charts and help explain, transform or debug programming work through its data-analysis tools. Users should remove secrets and unnecessary personal data, verify calculations and generated files, review code and dependencies, run production tests separately, monitor retention settings and retain human analytical and engineering responsibility.

Use authorized, minimal inputs and grant integrations the least access required. Configure privacy, retention, sharing, quality, accessibility, disclosure, export, governance, moderation and spending controls. Test representative cases, verify generated facts, calculations, citations, customer messages, code, trades and financial details, preserve originals and version history, and retain accountable human approval before publication, outreach, deployment or operational action.

Verified pricing: Free + from $20/mo. A free ChatGPT plan is available and paid individual access starts from approximately $20 per month, with business and enterprise options. Models, messages, file uploads, analysis capacity, subscription period, renewal and taxes vary.

Pros and Cons

Pros

  • Appears in current ChatGPT as data analysis rather than a separate product name
  • Runs Python to clean; transform; merge; and analyze uploaded data
  • Creates charts and tables from natural-language requests
  • Supports analysis across multiple uploaded files
  • Can recommend a suitable visualization for a dataset
  • Produces interactive bar; line; pie; and scatter charts in supported cases
  • Lets users inspect the generated analysis code
  • Makes generated charts and output files downloadable
  • Can import files from connected Google Drive sources when enabled
  • Can import files from Microsoft OneDrive and SharePoint when enabled
  • Helps non-programmers complete routine exploratory analysis
  • Lets experienced users refine calculations through follow-up instructions
  • Provides a sandboxed environment for executing Python
  • Supports customizable chart colors and presentation settings
  • Displays uploaded data in an interactive table
  • Can identify patterns and summarize findings from structured datasets

Cons

  • Code Interpreter is a legacy label for ChatGPT's current data-analysis capability
  • Availability and supported inputs vary by plan; model; account; and workspace
  • The model can choose an unsuitable statistical method; grouping; or visualization
  • Generated Python can contain calculation and logic errors
  • Users must verify formulas; filters; units; and missing-value treatment
  • Poorly structured files can produce incomplete or misleading analysis
  • Scanned tables and visually complex documents may not extract accurately
  • Charts can imply causation or certainty that the source data does not support
  • Uploads are governed by file-size; usage; and storage limits
  • Connected files remain constrained by the user's source-system permissions
  • Sensitive datasets require appropriate workspace controls and data settings
  • Individual-account content handling depends on the user's data-control choices
  • The execution environment may differ from a local production environment
  • Interactive behavior is limited to supported chart types
  • High-stakes conclusions still require qualified human review
  • Capabilities and limits can change; so current official documentation should be checked

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